Multimodal ctDNA profiling for cancer detection and monitoring in pan-cancer patients with advanced disease enrolled in the SHIVA02 trial

Multimodal ctDNA profiling significantly improves cancer detection rates in advanced disease, with a detectability of 93.8% using an integrative model.

Summary and findings

This study evaluated the effectiveness of multimodal ctDNA profiling in detecting cancer in 32 patients with advanced solid tumors enrolled in the SHIVA02 trial. The integration of epigenomic biomarkers with standard mutation-based ctDNA analysis improved detection rates. At baseline, the three-step integrative model achieved a ctDNA detectability of 93.8%.

Abstract

<h4>Background</h4> Liquid biopsy-based monitoring of circulating tumor DNA (ctDNA) holds promises for real-time assessment of tumor burden and treatment response in precision oncology. However, mutation-based approaches alone show limited sensitivity, particularly in low-shedding tumors. We evaluated whether integrating epigenomic biomarkers — specifically LINE-1 retrotransposon (L1PA) hypomethylation and copy number variation (CNV) — with standard mutation-based ctDNA analysis could improve cancer detection and longitudinal monitoring in patients enrolled in the SHIVA02 precision oncology trial. <h4>Methods</h4> We performed a retrospective analysis of 32 patients with advanced or metastatic solid tumors who received molecularly matched targeted therapies within the SHIVA02 trial ( NCT03084757 ). Plasma samples were collected at baseline and longitudinally every two months and at progression. Multimodal ctDNA profiling was performed using the DRAGON targeted NGS panel covering SNVs, indels and focal CNVs in 571 genes and the DIAMOND assay profiling L1PA methylation and genome-wide CNV. A three-step classification algorithm integrating maximum variant allele frequency (MaxVAF), L1PA methylation-based cancer probability (MethP Cancer ), and genome-wide CNV scores was developed to maximize ctDNA detectability. <h4>Results</h4> At baseline, mutation-based profiling detected ctDNA in 62.5% of patients. L1PA hypomethylation alone identified ctDNA in 78.1% of patients, including cases with undetectable mutations. The three-step integrative model increased overall detectability to 93.8%. Concordance analyses between tumor tissue and plasma revealed that 80.6% of mutations and 60% of CNVs identified in tumor biopsies were detectable in matched ctDNA. The three modalities showed limited pairwise correlation at baseline, supporting their complementarity. Longitudinal analysis demonstrated that changes in MaxVAF, MethP Cancer , and L1PA CNV scores over time were informative on treatment response across tumor types, with ctDNA detected in 94.5% of samples collected at disease progression. In selected patients, ctDNA alterations preceded radiological progression by four months. <h4>Conclusions</h4> Multimodal ctDNA profiling integrating mutation analysis, CNV profiling, and LINE-1 hypomethylation substantially improves ctDNA detection at baseline and during treatment in a pan-cancer precision oncology setting. These complementary genomic and epigenomic biomarkers provide a more comprehensive and dynamic assessment of tumor burden than single-modality approaches, supporting prospective validation in larger cohorts for integration into precision oncology workflows.

Key findings

• Not reported in abstract.

Limitations

• Not reported in abstract.

Elsewhere in the PE 22-28 corpus